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Keywords = complex-shaped stone products

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19 pages, 2276 KiB  
Article
Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm
by Qitao Tian, Runshu Peng and Fuzeng Wang
Appl. Sci. 2025, 15(15), 8610; https://doi.org/10.3390/app15158610 (registering DOI) - 3 Aug 2025
Viewed by 76
Abstract
To tackle the challenges of detecting complex cracks on large stone slabs with noisy textures, this paper presents the first domain-optimized framework for stone slab cracks, an improved semantic segmentation model (YOLOv8-Seg) synergistically integrating U-NetV2, DSConv, and DySample. The network uses the lightweight [...] Read more.
To tackle the challenges of detecting complex cracks on large stone slabs with noisy textures, this paper presents the first domain-optimized framework for stone slab cracks, an improved semantic segmentation model (YOLOv8-Seg) synergistically integrating U-NetV2, DSConv, and DySample. The network uses the lightweight U-NetV2 backbone combined with dynamic feature recalibration and multi-scale refinement to better capture fine crack details. The dynamic up-sampling module (DySample) helps to adaptively reconstruct curved boundaries. In addition, the dynamic snake convolution head (DSConv) improves the model’s ability to follow irregular crack shapes. Experiments on the custom-built ST stone crack dataset show that YOLOv8-Seg achieves an mAP@0.5 of 0.856 and an mAP@0.5–0.95 of 0.479. The model also reaches a mean intersection over union (MIoU) of 79.17%, outperforming both baseline and mainstream segmentation models. Ablation studies confirm the value of each module. Comparative tests and industrial validation demonstrate stable performance across different stone materials and textures and a 30% false-positive reduction in real production environments. Overall, YOLOv8-Seg greatly improves segmentation accuracy and robustness in industrial crack detection on natural stone slabs, offering a strong solution for intelligent visual inspection in real-world applications. Full article
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12 pages, 3349 KiB  
Article
Effect of Machining Trajectory on Grinding Force of Complex-Shaped Stone by Robotic Manipulator
by Fangchen Yin, Shatong Wu, Hui Huang, Changcai Cui and Qingzhi Ji
Machines 2022, 10(9), 787; https://doi.org/10.3390/machines10090787 - 8 Sep 2022
Cited by 3 | Viewed by 1944
Abstract
Complex-shaped stone products (CSSPs) have become stone products with high added economic value due to their complex overall shape, outline structure, and various curved surfaces. Recently, robotic manipulators—pieces of intelligent machining equipment—equipped with grinding end-effectors have significantly replaced handheld equipment and have also [...] Read more.
Complex-shaped stone products (CSSPs) have become stone products with high added economic value due to their complex overall shape, outline structure, and various curved surfaces. Recently, robotic manipulators—pieces of intelligent machining equipment—equipped with grinding end-effectors have significantly replaced handheld equipment and have also shown significant advantages in grinding efficiency and modeling flexibility. However, natural stone generally has the characteristics of poor craftsmanship and low rigidity. Improper control of the grinding force while grinding can easily cause the stone blank to break and scrap the workpiece. Therefore, in this study, we consider CSSPs and examine their surface curvature characteristics. The matching relationship between surface characteristics and machining trajectory is studied through simulation. Furthermore, the grinding force fluctuation in the finishing is optimized, and the optimal machining trajectory of the finishing process is determined to improve the surface profile error. Then, the simulation reliability is verified through experiments. The results show a 52.8% reduction in the grinding force fluctuation and a 36.9% reduction in the surface profile error after machining. Full article
(This article belongs to the Section Advanced Manufacturing)
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